Resolving three-dimensional anisotropic structure with shear wave splitting tomography

Transcription

1 Geophys. J. Int. (2008) 173, doi: /j X x Resolving three-dimensional anisotropic structure with shear wave splitting tomography David L. Abt and Karen M. Fischer Department of Geological Sciences, Brown University, 324 Brook St. Box 1846, Providence, RI 02912, USA. David Accepted 2008 February 14. Received 2007 December 20; in original form 2007 June 11 SUMMARY Shear wave splitting observations are a commonly used tool for inferring anisotropy and flow within the Earth s interior. Here we present the development and validation of a new technique for imaging anisotropy in the upper mantle using local events shear wave splitting tomography (SWST). The mantle is parametrized as a 3-D block model of crystallographic orientations with the elastic properties of olivine and orthopyroxene, and both orthorhombic and hexagonal symmetries are tested. To efficiently forward calculate splitting, the Christoffel equation is used to progressively split the horizontal components of a synthetic wavelet in each block of the model, and predicted shear wave splitting parameters are obtained with an eigenvalue minimization technique. Numerically calculated partial derivatives are utilized in a linearized, damped least-squares inversion to solve for a best-fitting model of crystallographic orientations. To account for the non-linear properties of shear wave splitting, the inversion is applied iteratively and partial derivatives are recalculated after each iteration. A starting model that incorporates information from predicted splitting parameters is found by spatially averaging fast directions and the ratio of observed-to-predicted splitting times. Models from inversions utilizing this average starting model reach lower misfit levels than do inversions with a random or uniform starting model. Modelling results using synthetic data from several anisotropic structures (i.e. sharp lateral and vertical variations in anisotropy) both within an idealized and a real (Nicaragua Costa Rica) subduction zone illustrate the capabilities and limitations of SWST. With a station spacing of 25 km in an idealized subduction zone containing uniformly spaced events down to 225 km, both the azimuth and dip of crystallographic axes are resolvable to a depth of km and lateral heterogeneities in anisotropy on a scale of 50 km at arc and forearc distances from the trench are retrieved. Spatial resolution of anisotropy at scales of 75 km is possible further into the backarc above 150 km depth. The geometry of stations and observed seismicity in the Nicaragua Costa Rica subduction zone yields partial to good resolution at scales of km beneath the forearc, arc and limited regions of the backarc down to 100 km, and resolution at coarser scales is possible in wider regions beneath the backarc. Given the distributions of seismic sources within many subduction zones and the advances in broad-band seismic array deployments, this new method offers a powerful means with which to constrain the orientation of anisotropic fabric in the upper mantle. Key words: Seismic anisotropy; Seismic tomography; Subduction zone processes; Dynamics of lithosphere and mantle. GJI Seismology 1 INTRODUCTION Shear wave splitting is a widely used seismological observation that provides a powerful diagnostic of mantle anisotropy. However, the resolving power of splitting measurements can be greatly enhanced through the use of tomographic imaging, which allows for a quantitative characterization of the lateral and vertical distribution of anisotropy, thus moving beyond simplified qualitative interpretations of mantle fabric based on individual splitting measurements. Recent studies have made advances towards anisotropy tomography using splitting intensity measurements from long-period teleseismic SKS waves (Favier & Chevrot 2003; Chevrot et al. 2004; Chevrot 2006; Long et al. 2008). We have chosen to focus on a different problem the tomographic inversion of shear wave splitting parameters (i.e. fast polarization direction, φ, and delay time, dt) (e.g. Silver & Chan 1991) from local events for 3-D models of anisotropy. Shear wave birefringence (i.e. splitting) observed at the Earth s surface represents the integrated effects of all anisotropic structure encountered by a wave during propagation from source to receiver (or core mantle boundary to receiver for SKS and related phases). C 2008 The Authors 859

2 860 D. L. Abt and K. M. Fischer Without any a priori assumptions regarding Earth structure, a single splitting measurement does not fully constrain the location, orientation or magnitude of anisotropy along the ray path. Unlike SKS waves, which reach the surface with near-vertical incidence (<10 ), local S waves can produce reliably measured splitting at incidence angles up to 35 (i.e. within the shear wave window), allowing for greater sampling of anisotropy as a function of incidence angle and greater crossing ray coverage within a volume of interest. Subduction zones offer an ideal environment to employ shear wave splitting tomography (SWST) because seismic sources within a subducting plate are widely distributed in depth directly beneath the anisotropic mantle wedge, and with a dense seismic array, the differential paths from local S waves allow for more precise imaging than with teleseismic phases alone. Several subduction zones manifest substantial variations in splitting fast directions, which in turn suggest the presence of complex anisotropic structure and mantle flow. Local S wave observations in South America (Polet et al. 2000; Anderson et al. submitted), New Zealand (Audoine et al. 2004; Morley et al. 2006), Tonga Fiji (Smith et al. 2001), the Marianas (Fouch & Fischer 1998; Volti et al. 2006; Pozgay et al. 2007), Ryukyu (Long & van der Hilst 2006), Japan (Iidaka & Obara 1995; Nakajima et al. 2006), Kamchatka (Levin et al. 2004), the Aleutians (Yang et al. 1995) and Nicaragua Costa Rica (Abt et al. 2006) show evidence for anisotropy that differs substantially from the predictions of flow driven by motion of a subducting plate (i.e. simple 2-D corner flow in the mantle wedge), in particular if the fast symmetry axis of olivine is assumed to be parallel to the flow direction (i.e. A-type dislocation creep, as in Zhang & Karato 1995). In addition, many SKS studies have revealed arc-parallel fast directions in subduction zones (Russo & Silver 1994; Fouch & Fischer 1996; Polet et al. 2000; Peyton et al. 2001; Anderson et al. 2004; Huang et al. 2006; Long & van der Hilst 2005) whereas others display fast directions more consistent with 2-D entrained mantle flow (Currie et al. 2004). However, the interpretation of SKS fast directions is problematic because of uncertainty as to whether splitting in these phases primarily reflects anisotropy above or below the slab. Better constraints on shallow anisotropy from local S wave tomography will help to resolve this ambiguity. Two problems complicate the interpretation of local shear wave splitting in subduction zones in terms of mantle flow. First, most paths sample the slab, mantle wedge and upper plate, making it difficult to simply correlate observed splitting with anisotropy in any one of these regions. Second, the relationship between strain (i.e. flow) and lattice-preferred orientation (LPO) in mantle minerals has been shown experimentally (Bystricky et al. 2000; Jung & Karato 2001; Holtzman et al. 2003; Mainprice et al. 2005; Jung et al. 2006; Katayama & Karato 2006) and in field observations (Mehl et al. 2003; Mizukami et al. 2004) to be dependent on pressure, temperature, melt and water content and stress. Resolving the 3-D distribution of anisotropy will allow more informed interpretations regarding deformation processes and strain geometries in the wedge versus the slab and upper plate. The inverse problem posed in SWST is similar to isotropic velocity tomography in many respects. However, a non-linear dependence exists between the orientation of anisotropy and the polarization and propagation direction of shear waves. Resolution of anisotropic structure in spite of this non-linearity is the primary focus of this investigation, and we address the problem with the numerical calculation of partial derivatives applied in an iterative, damped and linearized inversion. We present the development of this approach to anisotropy tomography and validate it using a variety of subduction zone examples. Although we apply the method to local S waves, it can be easily extended to include SKS splitting, given assumptions about the maximum depth of anisotropy in the mantle. Because shear wave splitting observations require shear wave particle motions and high signal-to-noise ratios, quality splitting measurements are observed far less frequently than are P and S traveltimes. Thus, both the quantity of data available and the spatial coverage offered by shear wave splitting measurements are significantly reduced relative to velocity tomography. We have therefore conducted inversions using synthetic data from two event station distributions: (1) an idealized (i.e. uniformly-spaced) distribution of events and stations within an artificial subduction zone and (2) a real distribution of paths in a shear wave splitting data set from the Central American subduction zone (Abt et al. 2006). In Hoernle et al. (2008), as well as a forthcoming manuscript, we present the application of this method and its results to actual shear wave splitting observations from the Tomography Under Costa Rica And Nicaragua (TUCAN) seismic array in Nicaragua and Costa Rica. The specific objective of this paper is to demonstrate the ability of our combined forward modelling and inversion approach to retrieve different geometries of anisotropic structures in subduction zones. Following the discretization of a model space (Section 2.1) and definition of how the anisotropic Earth will be represented (Sections ), the process of testing the SWST method begins with the creation of a synthetic (i.e. known ) structure (Section 5.1) that we hope to recover by inverting synthetic observations calculated using a forward model of shear wave splitting (Section 3.1). Synthetic splitting parameters from the known model are determined, a starting model, which differs from the known model, is then defined (Section 4.1) and predicted splitting parameters are calculated from this starting structure. A linearized least-squares inversion (Section 4.2) is used to iteratively find changes to the starting model that result in a new model that yields predicted splitting parameters which more closely match those from the known structure. Incomplete sampling of the model (common in the real world and to the examples explored here) does not allow for the actual known model to be recovered exactly, rather it forces us to accept a best-fitting model. Several aspects of the inversion process can be manipulated to increase the resolution of model parameters and retrieve the known structure more accurately (Sections 4.3 and 5.4). After illustrating the strengths of SWST with several examples (Sections ), we discuss the scales of structures that are resolvable with the event station distributions tested here and compare this method with other approaches to imaging anisotropy (Section 6). 2 MODEL PARAMETRIZATION 2.1 Model space The model space is parametrized into a 3-D array of blocks, with each block having a uniform crystallographic orientation and strength of fabric alignment throughout. The boundaries of the model space are defined to contain all sources and stations, although the method could easily be generalized to include SKS and related core phases. In this paper, we apply our method to two subduction zone cases. The first is an idealized subduction zone with a station spacing of 25 km in the along-arc and across-arc directions and a surface containing events that represents the top of a subducting plate extending to a depth of 225 km and dipping at either 45 or 60 (Fig. 1). In the second case, sources and stations correspond to the shear wave splitting data set obtained from the TUCAN broad-band seismometer experiment (Abt et al. 2006) in the Nicaragua Costa

3 Shear wave splitting tomography 861 Figure 1. Event and station locations in an idealized subduction zone model. Starting in the second layer, each block along a 45 angle contains either 1, 2, 3 or 4 events per block; 60 slab dips are also employed. The example shown here has two events per block, but the inversion results presented in Sections 5.5 and 5.6 all contain four events per block. We vary the number of events per block to assess the effects of increasing or decreasing the number and distribution of crossing rays. Only ray paths that have incidence angles within the shear wave window (<35 ) are actually used, and therefore, splitting measurements from a fraction of all ray paths are included in the inversion. For reference in the 45 case, one event per block yields 2022 event station pairs within the shear wave window, 4035 for two events per block, 6035 for three and 8070 for four. Initial polarizations are randomly generated and are the same for all rays coming from a single event. The cubic grid illustrates the extent of the model space. Rica subduction zone (Fig. 2) sources extend down to 220 km. The TUCAN experiment was a 20 month deployment that featured a relatively dense array (10 50 km station spacing) designed specifically to image the mantle wedge. We have tested inversions using a variety of block sizes, and here present results for a cube with sides of 25 km (i.e. a km 3 block). Although smaller blocks would allow for smallerscale variations in anisotropic structure and possibly yield a better fit to the data, the constraints on parameters in each block would decrease whereas computation time would increase. On the other hand, a larger block size may not allow for enough small-scale variation, leading to a poor fit with the data. There are regions, however, in both models (idealized and real), in which ray coverage is sparse, and in these cases, it is advantageous to consider non-cubic volumes composed of more than one km 3 block. The generation of model volumes larger than individual blocks is described in Section 4.3, and the effects on model resolution are discussed in Section Mineralogy and elastic coefficients We assume a mineralogy of 70 per cent olivine and 30 per cent orthopyroxene (opx) and use the elastic constants of Forsterite (Mg 1.8 Fe 0.2 SiO 4 ) (Anderson & Isaak 1995; Abramson et al. 1997) and Bronzite (Mg 0.8 Fe 0.2 SiO 3 ) (Frisillo & Barsch 1972), including their pressure and temperature derivatives (Table 1). The olivine coefficients at the surface are the average of those from Anderson & Isaak (1995) and Abramson et al. (1997) whereas the pressure derivatives are from Abramson et al. (1997) and the temperature derivatives are from Anderson & Isaak (1995). Frisillo & Barsch (1972) provide both pressure and temperature derivatives for opx. We assume a temperature profile based on a 30 km thick conductive upper layer, roughly approximating the lithosphere, which overlies an adiabatic mantle with a temperature of 1200 C at the base of the lithosphere. We have compared predicted splitting from this combination of elastic coefficients to predictions from other studies of the elastic properties of olivine (e.g. Graham & Barsch 1969; Kumazawa & Anderson 1969; Ismaïl & Mainprice 1998) and opx (e.g. Kumazawa 1969; Chai et al. 1997). We find that values are typically the same for φ and within 2 5 per cent of each other for dt. The inclusion of pressure and temperature derivatives has a larger effect on delay time (6 10 per cent difference) than on fast direction (almost none). At crustal depths, olivine opx elastic coefficients are not in general appropriate. For simplicity, we retain these coefficients throughout the model space, recognizing that within crustal regions, they should be taken as a proxy for more likely sources of

4 862 D. L. Abt and K. M. Fischer Figure 2. Events here correspond to those from actual high quality shear wave splitting measurements in Central America (Abt et al. 2006) and stations are from the recent TUCAN seismic experiment. The coordinate axes of the model space are rotated 35 W of north to characterize distances in approximately along-arc and across-arc directions. Note the distinct jogs in the volcanic arc and the non-uniform distribution of events relative to the idealized subduction zone. Slab seismicity appears very thick in the cross-section (bottom panel) because its strike changes along the arc and is not always aligned with the approximate along-arc/across-arc coordinate axes used here. crustal anisotropy (e.g. stress-induced cracks or deformation fabrics in crustal mineralogies). 2.3 Crystallographic symmetry Naturally deformed lithospheric peridotites (e.g. Mainprice & Silver 1993) often exhibit an LPO in which the a-axis [1 0 0] of olivine is aligned parallel to the c-axis [0 0 1] of opx, the b-axis [0 1 0] of olivine is parallel to the a-axis of opx and the c-axis of olivine is parallel with the b-axis of opx. Proportionally adding the stiffnesses (0.7 c olv ijkl copx ijkl ) in this orientation provides the elastic properties for our orthorhombic model mantle material. For simplicity, from here on the crystallographic axes mentioned will be those of olivine. Although olivine and opx are each intrinsically orthorhombic, naturally deformed rocks and deformation experiments have demonstrated that crystal aggregates may develop overall symmetries that are hexagonal or orthorhombic (Ismaïl & Mainprice 1998; Bystricky et al. 2000; Mehl et al. 2003; Jung et al. 2006; Michibayashi et al. 2006). We have therefore carried out inversions assuming both orthorhombic and hexagonal elastic coefficients. To simulate hexagonal symmetry using the orthorhombic single-crystal elastic constants of olivine and opx, the coefficients corresponding to the b- and c-axes must be combined into an isotropic plane perpendicular to the a-axis. We reduce the nine independent orthorhombic coefficients to the five independent hexagonal coefficients following Montagner & Anderson (1989). In the event of wave propagation parallel to the hexagonal symmetry axis, no splitting will occur, and the polarization direction will simply be that of the wave s initial polarization. However, for the cases examined here, the impact of this effect is negligible. Predicted fast directions for hexagonal and orthorhombic coefficients are typically nearly identical. Differences in splitting times between the two symmetries vary as a function of

5 Shear wave splitting tomography 863 Table 1. Single-crystal anisotropic elastic coefficients (C ij ) at 1 bar, pressure derivatives (δc ij /δp), and temperature derivatives (δc ij /δt) used to represent the upper mantle in the forward calculations of shear wave splitting. Olivine a,fo 90 (Mg 1.8 Fe 0.2 SiO 4 ) OPX b, Bronzite (Mg 0.8 Fe 0.2 SiO 3 )-4 ij C ij (at 1 bar) (GPa) δc ij /δp δc ij /δt (GPaK 1 ) C ij (at 1 bar) (GPa) δc ij /δp δc ij /δt (GPaK 1 ) Notes: The orthorhombic symmetry of olivine and opx requires nine elastic coefficients, which are typically given as elements in the 6 6 matrix C ij.by symmetry C 12 = C 21, C 13 = C 31 and C 23 = C 32. The C ij are part of the fourth-order stiffness tensor, c klmn, that fully describes the response of an elastic material to stress, and here, C 11 corresponds to the elastic coefficients for a P-wave travelling parallel the [1 0 0] axis, C 22 to the [0 1 0] axis and C 33 to the [0 0 1] axis. In our analysis, we assume a starting orientation of the olivine opx aggregate such that the [1 0 0]-axis of olivine is initially parallel to the x 2 direction, and the C ij presented here must be rotated as described in Fig. 3. Olivine C ij are averaged from Anderson & Isaak (1995) and Abramson et al. (1997), δc ij /δp are from Abramson et al. (1997) and δc ij /δt are from Anderson & Isaak (1995). The C ij, δc ij /δp and δc ij /δt of opx are from Frisillo & Barsch (1972). For completeness, the mapping between C ij and c klmn is provided here (e.g. Babuska & Cara 1991). C 11 = C 1111, C 12 = C 1122 = C 2211, C 44 = C 2323 = C 2332 = C 3232 = C 3223, C 22 = C 2222, C 23 = C 2233 = C 3322, C 55 = C 1313 = C 1331 = C 3131 = C 3113, C 33 = C 3333, C 13 = C 1133 = C 3311, C 66 = C 1212 = C 1221 = C 2121 = C a Anderson & Isaak (1995) and Abramson et al. (1997). b Frisillo & Barsch (1972). propagation direction and polarization, and although in general they are small, they may rise to roughly 25 per cent in certain cases. 2.4 Model parameters Crystallographic orientation is defined by three rotation angles: θ, ψ and γ (Fig. 3). θ represents the horizontal azimuth of the [1 0 0] axis, ψ is the plunge of the [1 0 0] axis from horizontal and γ is the plunge of the [0 0 1] axis. In the case of hexagonal symmetry, only the azimuth (θ) and dip (ψ) of the [1 0 0] axis (i.e. the symmetry axis) are needed; but for orthorhombic symmetry, the third angle (γ ) is required to completely orient the three orthogonal axes. Note that in the inversions discussed in Sections 5.5 and 5.6, we use only two angle parameters (θ and ψ). For the cases where orthorhombic symmetry is used, the c-axis of olivine is assumed to remain horizontal (i.e. γ = 0). Typical observed splitting times are much smaller than those predicted directly from the single-crystal elastic coefficients, most likely due to misalignment of crystals in real Earth volumes. We define a strength parameter, α, which is simply a scalar that simulates this dilution of anisotropy. Here α is not the same as the percent of shear wave anisotropy, δv s, often referenced in work involving seismic anisotropy [i.e. δv s = 2(V Sf V Ss )/(V Sf +V Ss ) from Ismaïl & Mainprice (1998), where V Sf is fast S-wave velocity and V Ss is slow S-wave velocity]. Differences in the amount of seismic anisotropy may be caused by variations in the percentage of oriented grains within a volume of mantle, but δv s also depends on the direction of wave propagation. 3 FORWARD MODELLING 3.1 Calculation of shear wave splitting parameters To enable the rapid calculation of predicted shear wave splitting parameters for each path, we use the approximate particle motion perturbation method of Fischer et al. (2000), only here we allow for 3-D distributions and orientations of anisotropy. The effects of anisotropy in each block encountered along a ray path are approximated by progressively rotating and time-shifting the horizontal components of a simple wavelet that has an initially linear horizontal particle motion (Fig. 4a). The non-linear process of splitting is dependent on the order in which anisotropic structure is encountered by a wave, and the method we use accounts for this behaviour. Although parametrized differently, this approach is equivalent to the complete (frequency-dependent) splitting operator of Rümpker & Silver (1998). We use a period of 1.5 s, which represents the typical frequency of local S waves in the Central American subduction zone (Abt et al. 2006). Initial polarizations for cases using the idealized event station distribution are randomly generated, whereas for the real event station locations, we use the initial polarizations estimated from actual observations (Abt et al. 2006). These real initial polarizations are given by the roughly linear horizontal particle motion obtained after correcting for the best-fitting pair of splitting parameters (Fig. 4a, bottom right-hand panel). As illustrated by the difference between the corrected linear particle motion and the input initial polarization, this measure of initial polarization is not exact, but represents a reasonable approximation when source mechanisms are unknown. By tracing each ray through the 1-D velocity/density model AK135 (Kennett et al. 1995), path lengths and direction cosines for each ray segment are obtained. Although we are solving for a 3-D anisotropic model, these ray paths are utilized throughout the inversion. This simplifying assumption mildly affects ray directions and path lengths within model blocks, but given the relatively short total paths and large model blocks in the cases studied here, the blocks a ray samples do not generally change. Splitting parameters calculated with the 1-D ray paths and the particle motion perturbation method are typically very similar to those calculated with a full

6 864 D. L. Abt and K. M. Fischer ith block is Figure 3. Model parameters and rotation scheme used in the forward modelling for orienting the crystallographic axes of olivine and orthopyroxene. ψ, γ and θ rotate around the x 1, x 2 and x 3 axes, respectively. The angles used to orient olivine and opx as described in the text (e.g. Mainprice & Silver 1993) are given in the table; in this case, the a-axis of olivine is parallel to x 2 and the b-axis is vertical. waveform method (Section 3.2), suggesting that overall ray bending effects are small. Polarization directions and phase velocities for each ray segment are given by the eigenvectors and eigenvalues, respectively, of the Christoffel matrix (m ij ) (e.g. Babuska & Cara 1991) m ij = 1 ρ(z) c ijkl(z) ˆn k ˆn l, (1) where ρ is density and a function of depth z (i.e. pressure and temperature), c ijkl is the elastic stiffness tensor of the olivine opx crystal (also a function of P and T), and ˆn is the propagation direction of the ray. With the exception of the case of hexagonal symmetry and propagation along the symmetry axis, m ij will possess three independent eigenvectors, characterizing the polarization direction of one compressional (P) and two shear (S fast and S slow ) waves. In the splitting calculation, fast shear wave polarization direction in a particular block,, is the horizontal projection of the actual S fast polarization direction in that block (we use here to distinguish it from the measured φ at the surface). Phase velocity is the square root of the eigenvalue corresponding to the eigenvector for that phase, and birefringence of the two shear waves results from the difference in their velocities. The time-shift, t, for a ray passing through the ( t = L i V 1 slow V fast) 1 αi, (2) where L i is the path length in the ith block, V fast and V slow are the fast and slow shear wave velocities, respectively, and α i is the strength of anisotropy (i.e. percentage alignment). In the first block, the two horizontal components of initially linearly polarized synthetic particle motion are rotated by, timeshifted in the frequency domain by t (fast component by t/2; slow by + t/2) and then rotated back to the original coordinate system. The resulting perturbed (e.g. elliptical) particle motion is then used as the input for the second block. After applying this procedure successively through each block touched by a particular ray, shear wave splitting parameters are determined from the resulting horizontal particle motion at the surface using the eigenvalue minimization method (e.g. Silver & Chan 1991). An example of the forward calculation and resulting waveforms is shown in Fig. 4. The eigenvalue minimization technique attempts to linearize the horizontal components of particle motion by performing a grid search over all possible azimuths (from 90 to +90 at 1 increments) and a range of splitting times (from 0 to 5sat0.01 s increments). For each trial combination of parameters, the horizontal components are rotated and time-shifted, and their correlation matrix is calculated. If the resulting particle motion were perfectly linear, this matrix would be singular; it would have only one nonzero eigenvalue, and its eigenvector would correspond to the direction of linear particle motion. For uniform anisotropy with a horizontal fast symmetry axis, the splitting fast direction is parallel to the fast axis. When a wave samples multiple regions of anisotropy with different orientations, for example two layers (e.g. Silver & Savage 1994) or the 3-D cases examined here, the φ and dt determined through this method are only apparent splitting parameters that reflect the integrated effects of all anisotropic structure sampled by the wave. In a process identical to that used for real observations, it is possible to quantify confidence limits for the synthetic splitting parameters calculated here. Each point in (φ,dt)-space is assigned the second (smaller) eigenvalue (λ 2 ) of the corresponding correlation matrix, and these values are contoured around the minimum (λ 2min ), with the 95 per cent confidence region given by λ 2 λ 2min [ 1 + ( f d f ) F ( f, d f, 95%)] 1/2, (3) where f is the number of free parameters (in case 2: φ and dt), d is the number of independent pieces of information and F(f, d f, 95 per cent) is the F distribution for 95 per cent confidence (Draper & Smith 1966). The number of independent pieces of information, d, is a function of the sampling rate, dominant frequency and amplitude of both the signal and noise; d is calculated by dividing the waveform window length used in each splitting calculation (10.24 s, here) by the minimum resolvable time (w), and w is the first zero crossing in the autocorrelation of the initial waveform. In a real waveform, the autocorrelated window would contain background noise. Errors (σ φ and σ dt ) are measured on the 95 per cent confidence contour at the maximum distance in the φ and dt directions, independently, from the best-fitting pair of splitting parameters (Fig. 4b). 3.2 Full-waveform comparison The predicted splitting parameters calculated with the particle motion perturbation method (simple wavelet) have been compared

7 Shear wave splitting tomography 865 Figure 4. Example of synthetic shear wave splitting used in the forward modelling (Section 3.1). (a) From left to right, an initial wavelet with linear horizontal particle motion ( 40 ) passes through the first anisotropic layer (orientation given by the three angles in the second lower box) and is split. The characteristic elliptical particle motion of a split wave is generated. In the third column, the result of passing the already split wave through a second anisotropic layer of different crystallographic orientation and strength of anisotropy can be seen. Owing to the nearly horizontal orientation of the a-axis, the fast and slow direction in both layers can easily be distinguished in the particle motion plots. The steps of rotating and time-shifting the waveforms are illustrated in the top right-hand side graph. The thin lines represent the result of rotating the E W (grey) and N S (black) components by φ ( 82 ). The thick line shows the fast waveform shifted by +dt/2 and the thin is the slow waveform shifted by dt/2. Note that the corrected linear particle motion does not exactly match the input particle motion. (b) Confidence contours around the best-fitting pair of splitting parameters and 1σ error bars for this example. North is up (i.e. 0) and ±90 = E W. with those obtained from full synthetic waveforms generated using the pseudospectral approach of Hung & Forsyth (1998). Fig. 5 shows results from two different models for which this comparison has been made. These models are divided into two regions, each with uniform elastic properties, which are separated by a vertical boundary. An event at 150 km depth is located 15 km to the right of the boundary. Wave propagation is 3-D and we calculate splitting using the horizontal components of motion. Splitting is shown (Fig. 5) along a line of surface stations normal to the vertical boundary and located at 80 km from the source in the boundary-parallel direction. In the first model (Fig. 5a), the left-hand side of the boundary is isotropic, and the right-hand side has a horizontal hexagonal symmetry axis oriented parallel to the y-direction. The anisotropic elastic coefficients have a 5 per cent contrast between the fast and slow shear wave velocities. Both the fast direction and magnitude of

8 866 D. L. Abt and K. M. Fischer Figure 5. Comparison of splitting parameters calculated from the particle motion perturbation method (blue) and full-waveform (red) pseudospectral synthetics (Hung & Forsyth 1998). (a) Each example has a double-couple source (strike = 0, dip = 45 and rake = 45 ) located at a depth of 150 km [x(east) = 160 km, y(north) = 150 km] and a vertical boundary [thick black line in panels (a) and (b)] at a horizontal distance of 145 km (x = 145 km) between the two sides of the model. Splitting is calculated along a line of stations at y = 70 km. (b) Model is isotropic on the left-hand side of the boundary and 5 per cent fast in the y-direction on the right-hand side. The splitting parameters show good agreement ( 0.11 s for dt, fast directions all within error of each other when measurement uncertainties are included). (c) Model with 5 per cent fast velocity in the x-direction on the left-hand side of the boundary and 5 per cent fast in the y-direction on the right-hand side. This large, sharp velocity contrast produces some ray bending and wave front interaction with the boundary, which leads to moderate differences ( 16, 0.26 s when measurement uncertainties are included) in splitting parameters between the two methods.

9 Shear wave splitting tomography 867 delay time measured with the two methods agree very well from one side of the boundary to the other, with the φ being identical (within error) and a maximum dt misfit of only 0.11 s. In the second model (Fig. 5b), 5 per cent anisotropy exists on both sides of the boundary; whereas the right-hand side is the same as in the first model, the symmetry axis is parallel to the x-direction on the left-hand side, resulting in a larger effective velocity contrast at the boundary. On the right-hand side (same side as the event), there is a good agreement in both φ and dt between the two methods. Moving across the boundary, the discrepancy between the two methods becomes larger (when accounting for measurement errors, up to 16 and 0.26 s). Overall, the simple wavelet method produces a more rapid shift in splitting times across the boundary. Given that the pseudospectral synthetics accurately reflect finite Fresnel zones and the simple wavelet method does not, this result is not unexpected. In the fullwaveform case, interaction of the wave front with the boundary results in some waveform distortion and possible ray bending, leading to the observed differences in measured splitting parameters. These effects are particularly evident in the case with 5 per cent anisotropy on either side of the boundary (Fig. 5c). However, an infinitely sharp contrast in velocity structure (i.e. crystallographic orientation) is not likely to exist in the mantle, meaning the magnitude of these effects may be overestimated here relative to real data sets. Typical contrasts in anisotropic structure in the models in this paper fall between the two examples in Fig. 5. Therefore, we believe the assumptions made in our forward calculation do not significantly undermine the ability of this method to capture the effects of anisotropy on shear wave particle motion. 4 INVERSION METHOD 4.1 Starting models An essential step is the choice of a starting model from which changes will be made to improve the fit between predictions and observations (in this case synthetic observations). We use three different starting models to test the dependence of the final model on this decision, and qualitatively, similar features resolved by inversions with different starting models can be taken as robust. (1) In the first model, we use an averaging technique that, for each block, fixes the a-axis in the horizontal plane (b-axis vertical in the orthorhombic cases) and assigns the azimuth of the a-axis to be the average fast direction ( φ ) of all rays that touch that particular block (Appendix A). The φ value for each ray in this case is calculated from a known distribution of anisotropy, using the forward calculation described in the previous section. Section 5.1 contains a complete explanation of the known structures. This approach differs from the spatial averaging method of Audoine et al. (2004) in that the averaging of fast directions is applied over a 3-D, rather than 2-D model space. The strength of anisotropy in this averaged model is determined with an averaging scheme that utilizes predicted splitting times per kilometre of ray path (Appendix A). In this way, we produce a 3-D starting model that assumes a direct relationship between horizontal a-axis orientation and fast directions, as well as between ray path length and strength of anisotropy. The average starting model for a structure containing a vertical sheet of arc-parallel a-axes surrounded by arc-normal a-axes within the idealized subduction zone is shown in Fig. 6; there is a clear visual correspondence between the known structure (Fig. 6a), the predicted splitting parameters (Fig. 6b) and the average starting model (Fig. 6c). (2) In the second starting model, crystal orientations and strengths of anisotropy are randomly generated for each model block (Fig. 7a). (3) The third type of starting model is one with a uniform crystal orientation and strength of anisotropy throughout (Fig. 7b); we choose horizontal a-axes oriented oblique to both arc-parallel and arc-normal (N S in the geographic coordinate system used for the Central American event and station locations) and a strength of 25 per cent. 4.2 Least-squares inversion The inversion of shear wave splitting parameters for anisotropy is accomplished by iteratively solving for the orientation and strength of anisotropy that minimize misfit with the synthetic observations through a linearized, damped, least-squares approach (Tarantola 1987). M iter+1 = M iter + C mm G T [ GC mm G T + C dd ] 1 δd iter, (4) where changes to a model, M iter, are made to produce a new, betterfitting model, M iter+1. δd iter is the data misfit and G is a matrix of partial derivatives, which are assumed to be linear with respect to the current model (M iter ). The form of G is G =. φ n α 1 dt n α 1 φ 1 α 1 φ 1 θ 1 φ 1 ψ 1 dt 1 α 1 dt 1 θ 1 dt 1 ψ 1. φ n θ 1 dt n θ 1. φ n ψ 1 dt n ψ 1 φ 1 φ 1 φ 1 α m θ m ψ m dt 1 dt 1 dt 1 α m θ m ψ m , φ n φ n φ n α m θ m ψ m dt n dt n dt n α m θ m ψ m with n observations and m model blocks. Here we show the case for three model parameters (α, θ,ψ), which is applicable to both hexagonal and orthorhombic symmetries if we assume the c-axis remains horizontal in the orthorhombic case. The synthetic shear wave splitting calculations described in Section 3.1 are used to establish the dependence of the splitting parameters (φ, dt) on the model parameters (α, θ, ψ, γ ). By perturbing each model parameter independently, linear partial derivatives that populate the G matrix for the inversion are computed by finite difference. The effects of changes in crystallographic axis orientation on splitting parameters are most non-linear when shear wave polarization is linear and coincident with one of the crystallographic axes. Highly non-linear behaviour occurs over only a relatively small portion of the entire parameter space and is most prevalent at the onset of the forward problem (i.e. in the first ray path segment) because the initial wavelet is linearly polarized. After passing through the first anisotropic block, it is quite unlikely that the resulting particle (5)

10 868 D. L. Abt and K. M. Fischer Figure 6. Illustration of the average starting model described in Section 4.1 and Appendix A. (a) The known structure in the example shown here is a vertical sheet of horizontal arc-parallel a-axes within the idealized subduction zone (45 Slab dip angle) surrounded by arc-normal a-axes dipping 30 away from the trench, which is at y = 0 km. The symmetry assumed here is hexagonal, and the model parameters used are α, θ and ψ. Vectors represent the a-axis of olivine and are plotted at model block centres with length corresponding to strength and colour indicating azimuth. (b) Predicted splitting parameters from the known structure, plotted as standard shear wave splitting vectors at ray path midpoints, with length corresponding to delay time and colour (and orientation) indicating fast direction. (c) a-axis orientations in the starting model after averaging the fast directions from panel (b) and determining the average strength from the splitting times. The mean strength misfit with the known structure is quite small (12.13 per cent) whereas the mean angular misfit of the a-axes is larger (34.28 ), because the a-axes in the starting model are horizontal. motion will be both linear and parallel to a crystallographic axis in the next block. If perturbations to axis orientations determined from the inversion are small, then these linear partial derivatives should be valid for most combinations of particle motion and LPO encountered in this analysis. Because crystallographic orientation, as well as propagation direction, in each block touched by a given ray determines the resulting particle motion, partial derivatives must be recalculated when significant changes to the model occur. We use a perturbation of 5 for each angle parameter when calculating finite difference partial derivatives for the inversion and recalculate partial derivatives when a particular angle parameter (θ, ψ, γ ) changes by more than 1 from where the partial derivative was last calculated. Strength of anisotropy is simply a scaling factor, and we use perturbations of 5 per cent for this parameter and recalculate partial derivatives when α changes by more than 1 per cent. C dd is a diagonal data covariance matrix containing the sum of the squares of the 1σ errors (see Fig. 4b) of both the observed (σ φ,obs and σ dt,obs ) and predicted (σ φ,pred and σ dt,pred ) splitting parameters C 2n 1,2n 1 = σφ 2 + σ nobs φ 2 npred C dd = (6) C 2n,2n = σdt 2 + σ 2 nobs dt, npred where n corresponds to the nth splitting measurement in the data set. The a priori model covariance matrix, C mm, may have both diagonal and off-diagonal non-zero values. The function of the diagonal elements of C mm is to damp the changes to model parameters between iterations so that the validity of the linear partial derivatives is not compromised and the addition of off-diagonal components

11 Shear wave splitting tomography 869 Figure 7. (a) Random and (b) uniform starting models in the idealized subduction zone. The view in panel (a) is down and oblique to the dipping surface of events. Unlike in the average (Fig. 6) and uniform (panel b) starting models, the a-axes in the random starting model are not restricted to the horizontal plane. The small circles are the synthetic hypocentres and the black triangles are the synthetic stations. establishes a correlation between model parameters. The use of C mm is discussed further in the following section. 4.3 Inversion parameters There are several variables that can influence inversion behaviour (e.g. starting model, damping, smoothing and block size) and although each and every combination has not been tested, we have tested many. The inversion parameters discussed here and the range of values tested for each are given in Table 2, and the effects of each on data and model fit are discussed in Section 5.4. A useful means of assessing the convergence toward a best-fitting model in a damped inversion is to relax damping (i.e. increase the a priori value of model parameter variance) after a certain number of iterations. If the inversion has reached the global minimum in misfit prior to relaxing damping, then increasing variance will not result in significant changes to the model. In contrast, if the prerelaxation model simply occupies a local minimum that has been found by virtue of overdamping, then the model may change considerably upon damping relaxation. Low variance values may result in overdamping and require too many iterations to converge, whereas higher values could result in large changes to the model parameters that may violate the assumption of linearity. In typical data inversions and the cases studied here, certain model regions are undersampled and anisotropic parameters in individual model blocks will not be well resolved. With the real event station distribution from Central America, this occurs in the backarc where rays sample the backarc wedge in only one direction, as well as on the fringes of the array. In the idealized subduction zone, ray coverage is substantially lower in the deep wedge furthest from the trench. We therefore experimented with two approaches that impose a correlation between specific blocks in poorly sampled regions. The goal is to increase the spatial scales over which anisotropy parameters are allowed to vary to the point where parameters (now representing these larger spatial scales) can be meaningfully resolved. In the first approach, we combine blocks that are individually sampled by very few rays into larger volumes that act as a single block (Figs 8 and 9). Including this additional information within the inversion is accomplished by adding equations that force the changes made to the model parameters in each of the original individual blocks included in a volume to be equal (Appendix B). The large volume constraints are added partway through the inversion, once enough iterations have been completed with the original individual blocks to achieve a stable solution and minimize data misfit; this occurs after iteration 25 (30) in the idealized (Central American) subduction zone inversions (Fig. 10). At this point, the individual blocks within a volume are assigned uniform values for crystallographic orientation and strength of anisotropy; we use an average for each model parameter weighted by resolution matrix values (Section 5.2) for these parameters from the iteration prior to combining the blocks. For the remaining iterations, the large volume constraint equations are applied and are assigned small a priori values of variance to increase their influence relative to the splitting data. In the second approach, non-zero off-diagonal components are added to C mm to produce spatial smoothing. This type of smoothing is often used to satisfy some a priori information regarding the continuity of physical parameters between model blocks (e.g. Nataf et al. 1986). That is, a change made to a certain model parameter will generate a simultaneous change in that parameter in a nearby block. When used, the form of spatial smoothing is Gaussian and applied only to blocks immediately adjacent to a particular block (m 0 ) and is based on the distance from the centre of each adjacent block to the centre of the central block ( ) ( ) C m,m0 = σ 2 2 m 0 exp, (7) where σm 2 0 is the a priori variance of m 0 and 0 is the minimum distance between two blocks (i.e. blocks sharing a face). In general, we do not see any physical reason for a correlation between

12 870 D. L. Abt and K. M. Fischer Table 2. Parameters tested to optimize the behaviour and results of the inversion. Inversion parameter Testing conditions Values tested Preferred value Initial model parameter variance, σm 2 0 All three starting models, uniform known structure, hexagonal symmetry, solving for α and θ, 25 3 km 3 blocks, both subduction zones, two events per block with a 45 slab in the idealized subduction zone, 15 iterations Damping relaxation All three starting models, columns known structure, hexagonal (α,θ,ψ) and orthorhombic (α,θ,ψ,γ ) symmetry, 25 3 km 3 blocks, Central America subduction zone, 20 and 40 iterations Variance for larger volumes, σ 2 V All three starting models, columns known structure, hexagonal symmetry, solving for α and θ, both the idealized and Central America subduction zones, 25 3 km 3 blocks, 20 iterations Correlation length, 0 (km) Average starting model, column(s) known structure, damping = 5, hexagonal symmetry, solving for α and θ, 25 3 km 3 blocks, one and two events/block in the idealized subduction zone, 45 slab, 15 iterations (no smoothing) Note: The basis for each variable is described in Section 4.3 and their effects on data and model misfit are discussed in Section 5.4. A brief description of the different conditions under which each of the variables was tested is also given here. As illustrated, we have tested multiple scenarios to understand the impact of these inversion variables. the different types of parameters used here, and when discussing spatial smoothing, we are referring only to smoothing between like parameters. 5 INVERSION EXAMPLES 5.1 Target structures To test how well the SWST method resolves different geometries of anisotropic structure (i.e. crystallographic orientation and strength of anisotropy), we have applied the method to synthetic data from five target models. These structures differ only slightly between our two source/station distributions: the idealized synthetic subduction zone and the TUCAN splitting data set from Nicaragua and Costa Rica. In all examples, the majority of the model will possess a certain crystallographic orientation (e.g. dipping arc-normal a-axes) while a portion of the model will be given a dramatically different orientation (e.g. horizontal arc-parallel a-axes). Specific examples of these models are shown in Sections 5.5 and 5.6. First, we conducted simple control inversions in which the entire known structure has a uniform orientation (Fig. 11). The second type of known structure contains a vertical column of blocks, km 2 laterally, with arc-normal (or arc-parallel) a-axes extending from the top to the bottom of the model, representing a rapid variation in crystallographic orientation over a finite lateral extent (Fig. 12). The third structure is an along-strike sheet of arc-normal (or arc-parallel) a-axes, one block wide, extending down from the volcanic front in the Central American model space and a horizontal distance of km from the trench in the idealized model space (Fig. 13). Holtzman et al. (2003) have shown that when melt permeability is reduced in deformed peridotite samples, melt concentrates in oriented bands and olivine a-axes within interband crystal lenses tend to orient perpendicular to the transport direction. The sheet structure we use is meant to simulate this behaviour in the wedge beneath volcanic centres where large melt fractions are most likely. The development of B-type olivine fabric (Jung & Karato 2001; Jung et al. 2006) in the cold wedge corner (Kneller et al. 2005) has been proposed as the source of observed arc-parallel fast directions in the forearc of Ryukyu and Japan (Long & van der Hilst 2005; 2006; Nakajima et al. 2006). Therefore, the fourth structure has a region of arc-normal (or arcparallel) a-axes within the shallow wedge corner (Fig. 14). The final structure is used to test the capability of the method to resolve depth variations in anisotropy and is a layer cake model in which a-axes above 50 km and below 150 km are given one orientation (e.g. arc-parallel) and a-axes between 50 and 150 km have the opposite azimuth. Structures dominated by arc-normal a-axes would be expected for a standard 2-D corner flow model with A-type slip in olivine (e.g. Zhang & Karato 1995). Zones with arc-parallel fast anisotropy would represent perturbations caused by small-scale flow (Behn et al. 2007), melt band formation (Holtzman et al. 2003), activation of a different slip system (Jung & Karato 2001) or simply fossilized anisotropic fabric. In contrast, the same mechanisms that perturb crystallographic orientation relative to a dominant flow-related fabric, could just as easily occur in the presence of principally arcparallel flow, and we have tested all five known structures using both arc-normal and arc-parallel dominant orientations. 5.2 Quantifying resolution Determining the regions of the model space in which parameters are well resolved is key to interpreting inversion results. In linear inverse problems, a straightforward means of investigating the resolution of a model is through the resolution matrix, R M (Tarantola 1987), R M = C mm G T [ GC mm G T + C dd ] 1G, (8) where the components of R M are defined in Section 4.2. The diagonal elements of R M reflect how well individual model parameters are resolved, whereas the trade-offs between different model parameters in different locations of the model are revealed by the off-diagonal terms. If a diagonal element of R M is exactly one, then the corresponding model parameter is perfectly well resolved; the diagonal element value declines toward zero as resolution of the model parameter decreases. The meaning of the resolution matrix R M is somewhat more complicated in a non-linear inversion problem such as the inversions for anisotropy studied here. Because the partial derivatives may change

13 Shear wave splitting tomography 871 Figure 8. Larger volumes generated to increase resolution of individual parameters in the Central American subduction zone. Blue blocks are those that remain independent after volumes (thick black lines) are formed. (a) Volumes used in the Central American subduction zone are not uniform and are based on hit counts. The red blocks are those within the volume that are actually touched by a ray and used in the inversion. (b) Example layer of larger volumes and independent blocks. The lateral extent of each larger volume does not change with depth, and each layer is 50 km thick, except for the surface layer, which is only 25 km. For clarity, the red blocks within the volumes are not shown here. significantly with each iteration, R M should be considered as reflecting the resolution of the solution only relative to the model from the previous iteration. In addition, when the large volume constraints are employed, the resolution matrix will reflect the values of a priori variance assigned to the constraint equations, but not the fact that the constraint equations correspond to zero values in the data vector (Appendix B). Nonetheless, the resolution matrix still contains valuable information about how the data sample the anisotropic parameters in a given model. We use the resolution matrix in two ways: (1) to define model blocks that should be combined into larger model volumes and (2) to define which model regions to display. In presenting the resulting tomographic models (Sections 5.5 and 5.6), we restrict the blocks shown to those in which all model parameters within the block have resolution values greater than 0.25 in the resolution matrix from the final iteration. We distinguish between levels of resolution by assigning different thicknesses to the vectors representing the a-axis orientation and anisotropy strength in each block. 5.3 Data and model misfit We characterize the fit of both the data and model with a mean, weighted misfit ( ε), and use the behaviour of both measures to choose best-fitting models from the different inversions. Data misfits, [(φ,dt) obs (φ,dt) pred ], are weighted by both the observed and predicted errors, calculated as in Section 3.2,

14 872 D. L. Abt and K. M. Fischer Figure 9. 3-D view of the larger volumes used in the idealized subduction zone km 3 volumes are made out of blocks farthest from the trench where the fewest crossing rays exist. Nearly all blocks within the volumes are touched but are not shown here for clarity. ε (φ,dt) = n i=1 (φ,dt) obs i (φ,dt) pred [ (φ,dt),obs i σ i + σ (φ,dt),pred ] 1 i n [ (φ,dt),obs i=1 σ i + σ (φ,dt),pred ] 1, i (9) where σ obs i are the 1σ errors associated with the ith synthetic observed splitting measurement calculated from the known structure and σ pred i are those associated with same ray but calculated from a model during the inversion. Typical errors are quite small (<5 and <0.05 s) because measurements made here are on synthetic wavelets (Fig. 4). Model misfit is calculated in a slightly different manner from the data in that we choose to compare only the resolvable portion(s) of the model to the known structure. For blocks in which all model parameters are well resolved, the mean, weighted strength misfit for each iteration is m ( ) j=1 α known j α model j σ α,model 1 j ε α = ( ) σ α,model 1, (10) j m j=1 where α model j is the strength of anisotropy in the jth block of the model, m is the number of well-resolved blocks and the 1σ errors (σ α,model j ) are given by the square root of the diagonal elements corresponding to α in the a posteriori model covariance matrix, C M, (Tarantola 1987) C M = C mm [C mm G ( ) ] T GC mm G T 1 + C dd G GC mm. (11) Orientation misfit, ε axis is described by the angular difference between the well-resolved a-axes in a model retrieved with the inversion and those in the known structure, and the weight assigned to each axis misfit is the mean 1σ error for all angle parameters used ε axis = m j=1 [ ( ( ) cos 1 a known a model)] σ θ j +σ ψ 1 j 2 m j=1 ( σ θ j +σ ψ j 2 ) 1, (12) where a known is the a-axis in the known structure and a known is the a- axis in the jth well-resolved model block. Note that here, and in the inversion examples shown in Sections 5.5 and 5.6, both the azimuth (θ) and dip (ψ) of the a-axis are solved for, but for inversions where only θ is used, σ ψ would be zero and the 2 in the denominator of the weight would become 1. Similarly, if γ is solved for, then σ γ would be added and the denominator would be Optimal inversion parameters Through extensive testing, the inversion parameters which most consistently result in the lowest data and model misfits, have been determined. The range of values tested and their preferred values are given in Table Model parameters As described in Section 2.4, three angle parameters (θ,ψ,γ ) are needed to fully orient the three crystallographic axes of the olivine opx crystal, but resolution testing has shown that γ is not as well resolved as θ and ψ with the limited ray coverage available in Central America, and therefore, we assume the [0 0 1] axis to be horizontal (i.e. γ = 0) and only consider θ and ψ for the inversions with orthorhombic symmetry. This assumption does not affect the inversions using hexagonal symmetry.

15 Shear wave splitting tomography 873 Figure 10. Effects of large volumes on model parameter resolution. Values shown are those of the diagonal element of the resolution matrix corresponding to the strength parameter, α (panels a and b), and the a-axis azimuth, θ (panels c and d), at iteration 25 (a) and 30 (c) just prior to adding the large volume constraints and at iteration 26 (b) and 31 (d) just after applying the constraints. The outline of the volumes is shown with thick, light grey lines. Note that in panels (c) and (d), some volumes do not have blocks at 75 km that are touched by a ray, and therefore, the region displays a resolution value of zero Initial model parameter variance It is found that an a priori model parameter variance of 1 provides the best compromise between stability, convergence rate and number of resolvable blocks. In terms of model parameters, such a value means that we assume each parameter (either an angle or strength) is known within ±1 (degree or per cent). Additionally, we find that relaxing damping (variance) to a value of 10, results in a notable increase in the number of well-resolved blocks without significantly decreasing inversion stability Damping relaxation Relaxing damping from an initial value of 5 to a value of 20 results in an increase in the number of well-resolved blocks, but both data and model misfit also increase. Such behaviour demonstrates that relaxing to a value of 20 permits model parameter changes that violate the assumption of linearity to a degree that prevents convergence to a stable solution. However, relaxation of damping from an initial value of 1 to a value of 10 produces no significant change in misfit while still increasing the number of well-resolved blocks. Therefore, we choose to use an initial model parameter variance value of 1 and relax damping to 10. For the idealized subduction zone, damping is relaxed after 15 iterations, and for the Central American inversions, relaxation occurs after 20 iterations Large volume constraints The generation of larger model volumes from individual model blocks (Figs 8 and 9 and Appendix B) allows well-resolved anisotropic parameters to be retrieved in portions of the model where parameters for individual km 3 blocks are not as well resolved (Fig. 10); possible spatial variations in anisotropy are obviously made coarser in the regions where these constraints are applied. Large volume constraints are introduced at intermediate iterations (once stable solutions for the original individual blocks were obtained). The effect of the large volume constraints can be partially assessed by comparing values of the resolution matrix diagonals for a-axis azimuth between the intermediate iteration just before the large volume constraints are introduced (Figs 10a and c) and the next iteration (Figs 10b and d); this example corresponds to the case of uniform anisotropy with a dipping a-axis (e.g. Fig. 11). In backarc mantle regions where the large volume constraints combine groups of individual blocks, the resolution matrix diagonal for each large volume is larger than those of the individual blocks that comprise it. Note that if a portion of a large volume is untouched by any shear wave paths, that region is shown as having zero resolution (dark blue). We find that model structure at the iteration where the large volume constraints are introduced plays a significant role in how well the final retrieved model matches the known structure. In particular,

16 874 D. L. Abt and K. M. Fischer Figure 11. Inversion results from the uniform structure for the idealized event station distribution with a slab dip of 45. Vectors are projected onto a surface, along-arc and across-arc plane to provide a quasi-3-d view of the model and data. The models of anisotropy (i.e. olivine a-axes) are on the left-hand side and the predicted splitting parameters (i.e. splitting vectors plotted at ray path midpoints) from each model are on the right-hand side. The middle row shows the average starting model and splitting parameters; note that all blocks touched by at least one ray are displayed in the known and starting models. The bottom row shows the well-resolved blocks in the final model along with the predicted splitting parameters. Thicker vectors in the final model represent higher resolution of model parameters in that block, and vectors are shown only when all model parameters in a given volume achieve a resolution matrix diagonal value greater than The table indicates initial and final data and model misfits.

17 Shear wave splitting tomography 875 Figure 12. Inversion results from the column structure for the idealized event station distribution with a slab dip of 45. See caption from Fig. 11 for full description.

18 876 D. L. Abt and K. M. Fischer Figure 13. Inversion results from the sheet structure for the idealized event station distribution with a slab dip of 60. See caption from Fig. 11 for full description.

19 Shear wave splitting tomography 877 Figure 14. Inversion results from the wedge corner structure for the idealized event station distribution with a slab dip of 60. See caption from Fig. 11 for full description.

20 878 D. L. Abt and K. M. Fischer inversions using the average starting model achieve a much lower model misfit than those with either random or uniform starting models. This is not surprising, given that the regions of the model space influenced most by adding the large volume constraints are those in which model parameters are not individually well resolved. Unlike with the average starting model, which is initially similar to the known structure (Fig. 6), inversions with the random and uniform starting models are less able to retrieve the known structure in the more poorly sampled portions of the model space. The degree to which the volumes affect the inversion is controlled by the variance assigned to the added equations in G (see Appendix B, eq. B4). Although the data added with these constraints do not influence the resolution matrix, their associated variance does. We find that an a priori variance of 10 1 reduces model and data misfit more than values of 10 3 and We conclude that using larger model volumes is very valuable in terms of expanding the region where model parameters can be meaningfully resolved, but that these constraints should be applied in conjunction with an average starting model Spatial smoothing As an alternative to the large volume constraints described in the previous section, we instead applied spatial smoothing through the addition of non-zero off-diagonal terms to C mm for several length scales ( 0 = km). Because 0 is the distance at which the value of C mm drops by e 0.5 (see eq. 7), light spatial smoothing is applied even when 0 is less than the individual block dimensions. For all 0, no notable improvement in model convergence is observed, and at 0 = 25 km (the distance between facing blocks), a significant increase in data and model misfit also occurs. Overall, this approach was much less effective at extending the region where anisotropic parameters are well resolved while keeping data misfit low. It was therefore discarded in favour of the large volume constraints. 5.5 Idealized subduction zone We first present several inversion examples from idealized subduction zone models to demonstrate the capability of the SWST method to resolve crystallographic orientations from local shear wave splitting measurements. Inversions for the target structures described in Section 5.1 are shown in Figs Each figure includes the known model, the splitting parameters from the known model, the starting model and splitting parameters and the final model and associated splitting parameters. The initial and final data and model misfits are also given in a table with each figure. The pseudo-3-d figures are produced by projecting the entire 3-D model onto each side of a cube, allowing visualization of the azimuth and dip of each a-axis in both along-arc and across-arc directions. Overlapping axes are seen because all vectors in the model are displayed. In the cases where a-axes are horizontal and exactly arc-normal (or arc-parallel), no vector will be visible in the along-arc (or acrossarc) cross-sections. The location of the trench and projection of the events used to represent the slab surface are displayed in the plot containing the splitting parameters from the target structure (subplot b). Each inversion has an initial model parameter variance of 1 for α, θ and ψ; all models have a horizontal c-axis (i.e. γ = 0). A total of 35 iterations are completed, damping is relaxed to 10 after 15 iterations, spatial smoothing is not applied and larger volumes (Section 4.3) are formed after 25 iterations with a variance of 0.1 (Fig. 10, Appendix B). All model blocks are shown in the known and initial models, whereas only well-resolved blocks are shown in the final models. The vectors representing a-axes and splitting parameters are all the same thickness except for subplot (e), in which vector width corresponds to the level of resolution (see Section 5.2) of model parameters in a particular block (e.g. thicker = better resolved). An example of the resolution of two model parameters (α and θ) is illustrated in Fig. 10 and is representative of resolution in other inversions; model parameter resolution does not change notably between the different inversion examples because propagation and polarization direction influences resolution more than model orientation. The control inversion example contains all arc-parallel a-axes dipping at 30 (Fig. 11a), uses the average starting model (Fig. 11c) and assumes hexagonal symmetry. The initial model is very close to the known structure in terms of a-axis azimuth and strength of anisotropy, but because a-axes are horizontal in the starting model the initial angular misfit is Initial fast direction and splitting time misfit are also quite low. The inversion decreases data misfit significantly ( for φ and s for dt), and although strength misfit increases from 9.43 to per cent, angular misfit is reduced to Importantly, a-axis dip is accurately recovered nearly everywhere in the model down to 150 km. This example demonstrates the effectiveness of the inversion and provides context to assess the inversions that attempt to retrieve heterogeneous anisotropic structure. The target structure with a vertical column of horizontal arcnormal a-axes surrounded by dipping arc-parallel a-axes is shown in Fig. 12(a). In map view, the starting model in this example displays only a slight perturbation in the dominantly arc-parallel a-axes where the column exists and almost no indication of the column in either cross-section (Fig. 12c). Following the inversion, the column is clearly visible in both map view and cross-section (Fig. 12e). Note that the arc-normal a-axes in the column remain nearly horizontal whereas the dip of the surrounding arc-parallel a-axes is retrieved down to 75 km. There is a notable increase in strength of anisotropy misfit, but angular misfit is cut by more than half and data misfit is also reduced. The next example is for a structure in which dipping arc-parallel a-axes dominate and a one block wide sheet of horizontal arc-normal a-axes extends down from the surface (Fig. 13a). In this case, the dip of the slab is 60 (Fig. 13b) and the symmetry used is orthorhombic. The average starting model here (Fig. 13c) exhibits, in map view, a closer resemblance to the known model than for the column structure (Fig. 12c), but there is no indication of a vertically continuous feature. The inversion, however, is able to accurately reproduce both the horizontal arc-normal a-axes and dipping arc-parallel a- axes down to depths of at least 150 km (Fig. 13e). Angular misfit reduction is commensurate with the visual agreement between the target and initial/final models and data misfits improve, but again strength misfit increases. For the wedge corner target structure, we use a dominantly arcnormal structure (dipping) with horizontal arc-parallel a-axes above 100 km and out to 100 km from the trench, resulting in a notch of arc-parallel structure in the wedge corner (Fig. 14a). Such a geometry allows us to examine the ability of the inversion to retrieve vertical variations in anisotropy close to the slab. The starting model displays vertically continuous structure that does not reflect the notch in the wedge corner (Fig. 14c); the final model clearly reveals the notch, and the dip of arc-normal a-axes further from the trench is reasonably well recovered down to 150 km (Fig. 14e).

21 Shear wave splitting tomography 879 Figure 15. Inversion results from the uniform structure with the Central American event station distribution. See caption from Fig. 11 for full description. 5.6 Central American subduction zone The inversion results in Figs illustrate that the SWST method can also be utilized with less uniform event and station distributions that are more typical of real data. These inversions use the event station locations from the TUCAN shear wave splitting data set in Nicaragua and Costa Rica. They are run for 40 iterations, damping is relaxed from a value of 1 to a value of 10 at iteration 20, no spatial smoothing is used and larger volumes are generated in less wellresolved regions (Fig. 8) after iteration 30. The variance applied to the equations forcing the correlation between these blocks is 0.1 (see Appendix B). As with the idealized subduction zone, adding

22 880 D. L. Abt and K. M. Fischer the large volume constraints increases the joint resolution of model parameters within groups of blocks (Fig. 10). For the control inversion (i.e. uniform structure), in this case arc-normal a-axes that dip 30 towards the backarc (Fig. 15a), we solve for α, θ and ψ using the average starting model and assume orthorhombic symmetry. [1 0 0]-axis azimuths in the starting model match the known structure quite well (Fig. 15c), with the exception of a handful of more arc-parallel a-axes in Costa Rica. However, because the known model contains dipping a-axes, the initial model has an average angular misfit of 31. A large decrease in data misfit ( in φ and s in dt) occurs as a result of the inversion. The well-resolved portion of the model space extends far into the backarc region, and resolution of a-axis dip is good to depths of roughly 75 km (Fig. 15e). In this case, both angular and strength misfits are reduced ( and per cent), although the decrease in angular misfit is not as dramatic as in the idealized inversion (Fig. 11) owing to the less dense sampling of the model. The first example with heterogeneous structure is for the model with columns of horizontal arc-parallel a-axes surrounded by dipping arc-normal a-axes (Fig. 16a). One column is found in Nicaragua beneath the dense cross-arc line of stations and the other is beneath the Costa Rican cross-arc line. Both columns are visible in the starting model (Fig. 16c), but the column in Costa Rica is smeared in the along-arc direction and the strength of arc-parallel anisotropy is too low in the column in Nicaragua. The columns in the final model are better defined, and a-axis dip is recovered beneath the forearc and arc and down to a depth of 75 km (Fig. 16e). Except for α, both data and model misfit are substantially reduced: for model axis misfit and from to 7.6 and 0.52 to 0.17 s for the data misfit. The layered structure is not as well resolved as any of the laterally varying structures (columns, sheet and wedge corner) both for the event and station distribution in the Central American subduction zone (Fig. 17) and the idealized subduction zone (not shown). However, although the final model (Fig. 17e) does not match significant portions of the known structure (Fig. 17a), it is considerably closer to the input structure than the uniform starting model. Despite its deviations from the known structure, the final model does reproduce the predicted splitting parameters quite well (Figs 17b and f). The difficulty in resolving this type of layered anisotropy with SWST is not unexpected given that splitting is the integrated consequence of all anisotropic structure along the ray path. Structures similar to this simple layer cake model would be better resolved with sampling from near-horizontal rays or with a more complete range of initial polarizations at typical incidence angles in subduction zones. Nonetheless, because this inversion example employs the uniform starting model, it illustrates that the SWST method still functions well with starting models farther from the true structure. 6 DISCUSSION The inversion examples presented in Figs clearly demonstrate that the SWST method is able to accurately retrieve heterogeneous, dipping crystallographic orientations in a mantle wedge-type environment. This result suggests that we can use SWST to image mantle fabric and make interpretations regarding deformation. Here we discuss several aspects of the inversion process and provide rationale for some of the decisions we have made. Although all three types of starting models have been tested for each structure, we have chosen to show the results from inversions using the average starting model (except in Fig. 17) because they reach lower levels of both data and model misfit and also display a much more consistent behaviour in terms of misfit reduction. An important benefit of the average starting model is that the portions of the model space which are sampled less (i.e. deeper areas farther from the slab) begin the inversion with a model orientation that results in better-fitting splitting parameters. The random and uniform starting models produce the same structure as the average starting model in the best-resolved areas but are unable to do so in more undersampled regions because of the non-linearity and non-uniqueness of the problem. Within the more densely sampled shallow wedge corner of the idealized subduction zone, variations in anisotropy at the scale of 50 km can be resolved to depths of km (e.g. Figs 12 and 13). Partial resolution at scales of km exists beneath the forearc, arc and limited regions of the backarc down to 100 km in the Nicaragua Costa Rica models (e.g. Figs 15 and 16). Importantly, in both cases, a-axis dip is at least partially recovered in these regions. The well-resolved portion of the model space can be extended to the backarc in both the real and idealized models by coarsening model block size through the generation of larger volumes (Section 4.3, Figs 8 and 9; Appendix B), but in doing so, the scale of resolvable variations in anisotropy increases to at least 75 km. In general, at distances of more than 50 km beyond the volcanic arc in Central America, lateral resolution is dramatically reduced (Figs 15 17), and in both subduction zone models (idealized and Central America), retrieval of structure in deeper regions, which are illuminated by fewer sources, is difficult. Multiple horizontal layers of differently oriented anisotropy are less well resolved, given the event and initial polarization distributions considered here. However, as demonstrated by the inversion example in Fig. 14, this limitation does not preclude the recovery of all vertical variations in anisotropy. Although data misfit and model angular misfit are systematically reduced in the inversions, model strength misfit (α) increases in many cases. The α misfit increase is possible because changes made to the starting model and all subsequent models are based solely on the misfit of predicted splitting parameters and not on how well a particular model matches the known structure. After the first iteration, both φ and dt misfits decrease until levelling out. These data misfits are slightly higher in regions where variations in anisotropic structure are greater (i.e. near the column/sheet/wedge corner). Overall, angular misfit of the a-axis also decreases smoothly. In contrast, α misfits typically increase in the first iteration and then decrease overall with subsequent iterations. We find that strength misfit does not improve for any of the damping values examined (1, 5 and 10) in the testing of model parameter variance. The strength of anisotropy in the final models is often larger than in the known models, and this result appears to be due to tradeoffs between non-uniform anisotropic structure (e.g. alternating arcparallel and arc-normal a-axes) and strength of anisotropy. That is, if the delay time for a particular ray is greater than that predicted from the known structure, then two types of changes to the model could reduce data misfit equally well: (1) the overall strength of anisotropy could be decreased or (2) the orientation of a-axes in two blocks sampled by the ray could be made more different, essentially cancelling out a certain amount of birefringence for that ray. In other cases, the strength misfit appears to be due to trade-offs in strength with depth. For example, with the uniform anisotropy target structure in the idealized subduction zone (Fig. 11), a-axis orientation is

23 Shear wave splitting tomography 881 Figure 16. Inversion results from the columns structure with the Central American event station distribution. See caption from Fig. 11 for full description. fairly well retrieved throughout the model, but anisotropic strength is underestimated in the deeper, less sampled portion of the model and overestimated at shallower depths. Most regions in the models are sampled well enough that these effects are minimized, but such trade-offs, coupled with the non-linearity of shear wave splitting, appear to allow for the existence of multiple data misfit minima in model parameter-space. Overall for the six successful inversion examples presented here (Figs 11 16), model non-uniqueness contributed to average uncertainties of per cent in anisotropic strength. When the subduction zone is uniformly well sampled (the idealized case), the average a-axis misfit is quite small (7 15 ), but even in the case of the path distribution from Central America, the